Maximize human resources efficiency by reducing distractions during high productivity periods

ABSTRACT

A method, computer system, and computer program product for reducing one or more distractions during a period of high productivity are provided. The embodiment may include receiving a plurality of user metadata. The embodiment may also include, in response to determining a user is in a high productivity state, analyzing the plurality of received user metadata for potential distractions to the high productivity state. The embodiment may further include, in response to identifying one or more potential distractions based on the plurality of analyzed user metadata, determining an appropriate modification to the one or more identified potential distractions. The embodiment may also include performing the determined appropriate modification.

BACKGROUND

The present invention relates, generally, to the field of computing, andmore particularly to user notifications.

Notifications may relate to messages transmitted and/or displayed to auser that alerts the user to an item. For example, if a user receives atext message on a mobile device, a notification may be displayed on thehome screen of the mobile device to alert the user of the received textmessage. A notification system may be software and hardware that has thecapability of delivering a message to a recipient from a sender.Notification systems may be interrelated with other systems to provideadditional capabilities to unrelated entities. For example, a calendarprogram may have notification system capabilities that allow anotification to be sent to a user when a calendar event is upcoming.

SUMMARY

According to one embodiment, a method, computer system, and computerprogram product for reducing one or more distractions during a period ofhigh productivity are provided. The embodiment may include receiving aplurality of user metadata. The embodiment may also include, in responseto determining a user is in a high productivity state, analyzing theplurality of received user metadata for potential distractions to thehigh productivity state. The embodiment may further include, in responseto identifying one or more potential distractions based on the pluralityof analyzed user metadata, determining an appropriate modification tothe one or more identified potential distractions. The embodiment mayalso include performing the determined appropriate modification.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates an exemplary networked computer environment accordingto at least one embodiment;

FIG. 2 is an operational flowchart illustrating a high productivitymoment detection process according to at least one embodiment;

FIG. 3 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 4 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate to the field of computing,and more particularly to user notifications. The following describedexemplary embodiments provide a system, method, and program product to,among other things, detect moments of high user productivity andimplement steps to protect the user's attention from distractions.Therefore, the present embodiment has the capacity to improve thetechnical field of user notifications by automatically delaying thetransmission of notifications or altering a user's schedule based ondetecting the user being in a high productivity state.

As previously described, notifications may relate to messagestransmitted and/or displayed to a user that alerts the user to an item.For example, if a user receives a text message on a mobile device, anotification may be displayed on the home screen of the mobile device toalert the user of the received text message. A notification system maybe software and hardware that has the capability of delivering a messageto a recipient from a sender. Notification systems may be interrelatedwith other systems to provide additional capabilities to unrelatedentities. For example, a calendar program may have notification systemcapabilities that allow a notification to be sent to a user when acalendar event is upcoming.

When working, any distractions or interruptions, such as interruptingindividuals, pop-up messages, or meetings, can result in a loss in trainof thought and a disrupted mindset thereby yielding suboptimal workingperformance and individual efficiency. Only by reducing potentialdistractions can individuals achieve consistent periods of highproductivity. As such, it may be advantageous to, among other things,implement a solution to maximize an individual's performance duringperiod of high productivity by ensuring distractions are minimized.

According to one embodiment, user metadata produced by computers andsmart devices used by an individual while performing job-related tasksmay be monitored to determine instances when a user is highlyproductive. Additionally, calendar details, persistent chatconversations on various applications, such as Slack® (Slack and allSlack-based trademarks and logos are trademarks or registered trademarksof Slack Technologies and/or its affiliates) and Mural® (Mural and allMural-based trademarks and logos are trademarks or registered trademarksof Tactivos, Inc. and/or its affiliates), web search history, andpersonal notes may be analyzed using various criteria to classify auser's focus and performance level. The classification metrics may bedefined using the user's most productive and unproductive moments ratherthan productivity-related information of colleagues or competitors. Theuser metadata may also be monitored for potential distractions to theuser's productivity and any distractions may be rated based onusefulness or importance to the user as well as by the amount ofdistraction provided to the user. Then, based on the metrics, a suitablefiltering/modification of any identified distractions may occur toensure maximum user productivity is maintained.

For example, if an employee has recently joined a software company andis learning a new software coding language with which the employeestruggles, ensuring the employee maintains focus while learning the newcoding language is imperative. In a situation where the employee hasmade a breakthrough in learning the language and has subsequentlywritten a number of lines of code that successfully pass several tests,allowing the employee to remain focused may be extremely beneficial tothe employee's productivity in completing job tasks and establishing asolid foundation for the coding language. However, the employee may havean upcoming meeting with a coworker to which the employee is about toreceive a notification. Upon analyzing metadata surrounding the meeting,it may be determined that the meeting may be rescheduled since allparticipants have open schedules later in the day, the meeting room isavailable when the participants are available, and the topic of themeeting is not of high importance. Therefore, rather than producing apop-up notification and posing a risk to the employee's highproductivity, an automated message may be sent to the scheduled meetingparticipants requesting that the meeting be rescheduled since theemployee is in a period of high productivity.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, method,and program product to analyze user metadata to determine instanceswhere an individual is presently in a period of high productivity andacting accordingly to ensure the user maintains high productivity untila distraction necessitated by the analyzed user metadata.

Referring to FIG. 1, an exemplary networked computer environment 100 isdepicted, according to at least one embodiment. The networked computerenvironment 100 may include client computing device 102 and a server 112interconnected via a communication network 114. According to at leastone implementation, the networked computer environment 100 may include aplurality of client computing devices 102 and servers 112 of which onlyone of each is shown for illustrative brevity.

The communication network 114 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. The communication network 114 may includeconnections, such as wire, wireless communication links, or fiber opticcables. It may be appreciated that FIG. 1 provides only an illustrationof one implementation and does not imply any limitations with regard tothe environments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

Client computing device 102 may include a processor 104 and a datastorage device 106 that is enabled to host and run a software program108 and a high productivity moment detection program 110A andcommunicate with the server 112 via the communication network 114, inaccordance with one embodiment of the invention. Client computing device102 may be, for example, a mobile device, a telephone, a personaldigital assistant, a netbook, a laptop computer, a tablet computer, adesktop computer, or any type of computing device capable of running aprogram and accessing a network. As will be discussed with reference toFIG. 3, the client computing device 102 may include internal components302 and external components 304, respectively.

The server computer 112 may be a laptop computer, netbook computer,personal computer (PC), a desktop computer, or any programmableelectronic device or any network of programmable electronic devicescapable of hosting and running a high productivity moment detectionprogram 110B and a database 116 and communicating with the clientcomputing device 102 via the communication network 114, in accordancewith embodiments of the invention. As will be discussed with referenceto FIG. 3, the server computer 112 may include internal components 302and external components 304, respectively. The server 112 may alsooperate in a cloud computing service model, such as Software as aService (SaaS), Platform as a Service (PaaS), or Infrastructure as aService (IaaS). The server 112 may also be located in a cloud computingdeployment model, such as a private cloud, community cloud, publiccloud, or hybrid cloud.

According to the present embodiment, the high productivity momentdetection program 110A, 110B may be a program capable of receiving andanalyzing metadata to identify periods of high user productivity, andmodify notifications and events accordingly and appropriately so ensurehigh productivity is maintained. The high productivity moment detectionmethod is explained in further detail below with respect to FIG. 2.

FIG. 2 is an operational flowchart illustrating a high productivitymoment detection process 200 according to at least one embodiment. At202, the high productivity moment detection program 110A, 110B receivesuser metadata. In order to properly determine when a user is in a highproductivity state and when potential distractions are upcoming, thehigh productivity moment detection program 110A, 110B may receive orcapture user metadata. The user metadata may be information captured bya computer, such as client computing device 102, capable of accessinginformation via a network 114 or capturing information directly from auser, such as biometric data captured by wearable technology. The usermetadata may include a current open software application, successfulcompletion of scheduled tasks, calendar details, chat history, usertyping speed, heart rate, perspiration level, blood pressure, facialexpression, pupil dilation, user focus direction, and hand movements.

In at least one embodiment, the high productivity moment detectionprogram 110A, 110B may calculate a baseline for the user when an initialbatch of user metadata is received. The baseline may be used by the highproductivity moment detection program 110A, 110B to determine when theuser is in a high productivity state through a comparison of thereceived user metadata with the baseline.

Then, at 204, the high productivity moment detection program 110A, 110Bdetermines if the user is in a high productivity state. According to oneimplementation, the high productivity moment detection process 200 maycontinue if the user is in a high productivity state. The highproductivity moment detection program 110A, 110B may determine when theuser is in a high productivity state by calculating a productivity scorethat is relative to the user's personal baseline using a weighted sum ofconcentration measures. The concentration measures may assign positivescores to items that indicate higher than average productivity. Forexample, in the field of software development, items of highproductivity may include well written source code identified throughcode analysis tools, functional and sample tests for new code aresuccessful, and recent window focus data (e.g., the user spending largeamounts of time with a window of an integrated development environmentactive). Conversely, items of low productivity may include negativescores to items that indicate lower than average productivity (e.g.,short breakthroughs in work separated by long periods of littlecontribution), quantity of lines of code produced, consistency rate ofwork produced, historical productivity data, historical schedule data,hormonal physiological effects, brain physiological effects, recentdevice focus data, track micro/macro-expressions, general health data,and current noise levels. In at least one embodiment, the highproductivity moment detection program 110A, 110B may have otherconsiderations when determining a user's productivity score, such asphysiological factors, environmental factors, and behavioral factors. Ifhigh productivity moment detection program 110A, 110B determines user isin a high productivity state (step 204, “Yes” branch), the highproductivity moment detection process 200 may continue to step 206 toanalyze the received user metadata for potential distractions to theuser high productivity state. If the high productivity moment detectionprogram 110A, 110B determines the user is not in a high productivitystate (step 204, “No” branch), the high productivity moment detectionprocess 200 may terminate.

Next, at 206, the high productivity moment detection program 110A, 110Banalyzes the received user metadata for potential distractions to theuser high productivity state. The high productivity moment detectionprogram 110A, 110B may be able to identify various distractions to theuser's high productivity state based on the received user metadata. Forexample, a computing device's notification system may be monitored forincoming notifications/applications attached to the notification systemthat may be a potential distraction. Similarly, the high productivitymoment detection program 110A, 110B may monitor currently executingprograms/applications/services with the capacity to produce pop-upwindows that can be potentially distracting to the user. Additionally,application volume settings may be monitored for notifications andalerts. Furthermore, the high productivity moment detection program110A, 110B may be capable of tracking the number of display screens todetermine a specific display screen a notification (e.g., a pop-upwindow) may appear on in relation to the user's current focus.

Then, at 208, the high productivity moment detection program 110A, 110Bdetermines if a potential distraction is identified in the analyzed usermetadata. According to one implementation, the high productivity momentdetection process 200 may continue if a potential distraction isidentified in the analyzed user metadata. If high productivity momentdetection program 110A, 110B identifies a potential distraction (step208, “Yes” branch), the high productivity moment detection process 200may continue to step 210 to determine an appropriate modification to theidentified potential distraction. If the high productivity momentdetection program 110A, 110B does not identify a potential distraction(step 208, “No” branch), the high productivity moment detection process200 may terminate.

Next, at 210, the high productivity moment detection program 110A, 110Bdetermines an appropriate modification to the identified potentialdistraction. The high productivity moment detection program 110A, 110Bmay respond differently to a potential distraction depending on a ratioof how useful the distraction may be to how distractive the distractionmay be. Each identified potential distraction may be ranked in terms ofpriority as well as the impact on user concentration if the distractionis permitted to occur. Various ratings for how distractive an item maybe may include a high rating for a pop-up message in the middle of adisplay screen, a medium rating for notifications in a corner of thedisplay screen or a loud noise playing through speakers, and a lowrating for a quiet notification noise playing through speakers. Variousratings for how useful an item may be may include high for an instantmessenger chat window initiated by the user rather than another chatparticipant or a pop-up window that includes keywords relevant to theuser's current work and a low rating for a notification of an upcomingmeeting to which the user's attendance is optional. By weighing howdistractive an item may be versus an item's usefulness, the highproductivity moment detection program 110A, 110B may be capable ofgraphing a distraction element within a sector of a 2-dimensionaldistractiveness vs. usefulness plane where each section of the plane maybe color coded to indicate instances when specific modifications may beperformed for a distraction element. For example, if a plane is dividedinto red, orange, and green sectors to indicate highly distracting,moderately distracting, and mildly distracting sectors, the highproductivity moment detection program 110A, 110B may determinedistraction elements graphed in red sectors should be filtered out andnot presented to the user, distraction elements graphed in orangesectors should be modified to be less impactful on the user, anddistraction elements graphed in green sectors should not be modified andallow corresponding notifications be transmitted to the user normally.

In at least one embodiment, modifications for moderately distractingnotification (e.g., items graphed to an orange sector in the precedingexample) may include reducing the volume of a sound notification,shifting a notification/window to the side, and selecting usefulinformation from a message/email and only displaying the selectedinformation. Modifications for highly distracting notifications (e.g.,items graphed to a red sector in the preceding example) may includepreventing a noise/pop-up/notification from executing until a latertime, automatically setting a user status to away, and rescheduling ameeting for a later date and/or time.

Then, at 212, the high productivity moment detection program 110A, 110Bperforms the determined appropriate modification. Once the highproductivity moment detection program 110A, 110B determines theappropriate modification to perform, the high productivity momentdetection program 110A, 110B may execute the modification. For example,if the appropriate modification is to disable a pop-up notification foran upcoming meeting to which the user's attendance is optional, the highproductivity moment detection program 110A, 110B may alter the settingsof the meeting notification to disable the display of the meetingreminder to the user.

It may be appreciated that FIG. 2 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements. For example, at times when a distraction arises, the highproductivity moment detection program 110A, 110B may silencenotifications, emails, pop-ups, and hide/dim windows that are unrelatedto the task being completed by the user. Additionally, the highproductivity moment detection program 110A, 110B may divert incomingphone calls, reschedule meetings, change calendar bookings and settings,and cancel or extend room bookings accordingly.

In at least one other embodiment, at time of low user concentration, thehigh productivity moment detection program 110A, 110B can produce alertsor prompts to help the user focus. For example, if user's distraction iscaused by browsing the internet, the high productivity moment detectionprogram 110A, 110B may highlight or bring work-related windows to theforeground. Additionally, by retrieving calendar data, the highproductivity moment detection program 110A, 110B may incentivize theuser to be more productive by reminding the user of an imminent deadlineor a recent success. For example, the high productivity moment detectionprogram 110A, 110B may display the phrase “Last Tuesday, you contributed100 lines of bug free code, breaking your previous personal best of 86lines!”

FIG. 3 is a block diagram 300 of internal and external components of theclient computing device 102 and the server 112 depicted in FIG. 1 inaccordance with an embodiment of the present invention. It should beappreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The data processing system 302, 304 is representative of any electronicdevice capable of executing machine-readable program instructions. Thedata processing system 302, 304 may be representative of a smart phone,a computer system, PDA, or other electronic devices. Examples ofcomputing systems, environments, and/or configurations that mayrepresented by the data processing system 302, 304 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, network PCs, minicomputersystems, and distributed cloud computing environments that include anyof the above systems or devices.

The client computing device 102 and the server 112 may includerespective sets of internal components 302 and external components 304illustrated in FIG. 3. Each of the sets of internal components 302include one or more processors 320, one or more computer-readable RAMs322, and one or more computer-readable ROMs 324 on one or more buses326, and one or more operating systems 328 and one or morecomputer-readable tangible storage devices 330. The one or moreoperating systems 328, the software program 108 and the highproductivity moment detection program 110A in the client computingdevice 102 and the high productivity moment detection program 110B inthe server 112 are stored on one or more of the respectivecomputer-readable tangible storage devices 330 for execution by one ormore of the respective processors 320 via one or more of the respectiveRAMs 322 (which typically include cache memory). In the embodimentillustrated in FIG. 3, each of the computer-readable tangible storagedevices 330 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices330 is a semiconductor storage device such as ROM 324, EPROM, flashmemory or any other computer-readable tangible storage device that canstore a computer program and digital information.

Each set of internal components 302 also includes a R/W drive orinterface 332 to read from and write to one or more portablecomputer-readable tangible storage devices 338 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the highproductivity moment detection program 110A, 110B, can be stored on oneor more of the respective portable computer-readable tangible storagedevices 338, read via the respective R/W drive or interface 332, andloaded into the respective hard drive 330.

Each set of internal components 302 also includes network adapters orinterfaces 336 such as a TCP/IP adapter cards, wireless Wi-Fi interfacecards, or 3G or 4G wireless interface cards or other wired or wirelesscommunication links. The software program 108 and the high productivitymoment detection program 110A in the client computing device 102 and thehigh productivity moment detection program 110B in the server 112 can bedownloaded to the client computing device 102 and the server 112 from anexternal computer via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 336. From the network adapters or interfaces 336, thesoftware program 108 and the high productivity moment detection program110A in the client computing device 102 and the high productivity momentdetection program 110B in the server 112 are loaded into the respectivehard drive 330. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 304 can include a computerdisplay monitor 344, a keyboard 342, and a computer mouse 334. Externalcomponents 304 can also include touch screens, virtual keyboards, touchpads, pointing devices, and other human interface devices. Each of thesets of internal components 302 also includes device drivers 340 tointerface to computer display monitor 344, keyboard 342, and computermouse 334. The device drivers 340, R/W drive or interface 332, andnetwork adapter or interface 336 comprise hardware and software (storedin storage device 330 and/or ROM 324).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 100 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 100 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes100 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers 500provided by cloud computing environment 50 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and high productivity moment detection 96.High productivity moment detection 96 may relate calculating aproductivity score from captured user metadata to identify periods ofhigh user productivity and modifying notifications appropriately toensure the user maximizes high productivity times.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A processor-implemented method for reducing oneor more distractions during a period of high productivity, the methodcomprising: receiving, by a processor, a plurality of user metadata; inresponse to determining a user is in a high productivity state,analyzing the plurality of received user metadata for potentialdistractions to the high productivity state; in response to identifyingone or more potential distractions based on the plurality of analyzeduser metadata, determining an appropriate modification to the one ormore identified potential distractions; and performing the determinedappropriate modification.
 2. The method of claim 1, wherein theplurality of user metadata is selected from a group consisting of acurrent open software application, a successful completion of one ormore scheduled tasks, a plurality of calendar details, a chat history, auser typing speed, a user heart rate, a user perspiration level, a userblood pressure, one or more user facial expressions, a user pupildilation level, a user focus direction, and one or more user handmovements.
 3. The method of claim 1, wherein determining the user is ina high productivity state further comprises: calculating a productivityscore relative to a user baseline using a weighted sum of a plurality ofconcentration measures, wherein the plurality of concentration measuresassign a positive score to items that indicate a higher than averageproductivity and assign a negative score to items that indicate a lowerthan average productivity.
 4. The method of claim 3, wherein the userbaseline productivity state is calculated based on an initial batch ofuser metadata.
 5. The method of claim 3, wherein the plurality ofconcentration measures to which a positive score is assigned areselected from a group consisting of a plurality of well written sourcecode identified through a code analysis tool, a plurality of functionaland sample tests for a plurality of new code are successful, and aplurality of recent window focus data, and wherein the plurality ofconcentration measures to which a negative score is assigned areselected from a group consisting of a low quantity of lines of codeproduced, a slow consistency rate of work produced, a plurality ofhistorical productivity data, a plurality of historical schedule data, aplurality of hormonal physiological effects, a plurality of brainphysiological effects, a plurality of recent device focus data, aplurality of track micro/macro-expressions, a plurality of generalhealth data, and a current noise level.
 6. The method of claim 1,wherein the appropriate modification is based on a comparison of ausefulness of the identified potential distraction to the user and anamount of distraction of the identified potential distraction to theuser.
 7. The method of claim 6, wherein the appropriate modification isselected from a group consisting of reducing a volume level of a soundnotification, shifting a notification or window to a side of a graphicaluser interface, selecting and displaying only a plurality of usefulinformation from a message, preventing a noise from playing until alater time, preventing a notification from executing until a later time,automatically setting a user status to away, and rescheduling a meetingto a later time.
 8. A computer system for reducing one or moredistractions during a period of high productivity, the computer systemcomprising: one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage media, andprogram instructions stored on at least one of the one or more tangiblestorage media for execution by at least one of the one or moreprocessors via at least one of the one or more memories, wherein thecomputer system is capable of performing a method comprising: receivinga plurality of user metadata; in response to determining a user is in ahigh productivity state, analyzing the plurality of received usermetadata for potential distractions to the high productivity state; inresponse to identifying one or more potential distractions based on theplurality of analyzed user metadata, determining an appropriatemodification to the one or more identified potential distractions; andperforming the determined appropriate modification.
 9. The computersystem of claim 8, wherein the plurality of user metadata is selectedfrom a group consisting of a current open software application, asuccessful completion of one or more scheduled tasks, a plurality ofcalendar details, a chat history, a user typing speed, a user heartrate, a user perspiration level, a user blood pressure, one or more userfacial expressions, a user pupil dilation level, a user focus direction,and one or more user hand movements.
 10. The computer system of claim 8,wherein determining the user is in a high productivity state furthercomprises: calculating a productivity score relative to a user baselineusing a weighted sum of a plurality of concentration measures, whereinthe plurality of concentration measures assign a positive score to itemsthat indicate a higher than average productivity and assign a negativescore to items that indicate a lower than average productivity.
 11. Thecomputer system of claim 10, wherein the user baseline productivitystate is calculated based on an initial batch of user metadata.
 12. Thecomputer system of claim 10, wherein the plurality of concentrationmeasures to which a positive score is assigned are selected from a groupconsisting of a plurality of well written source code identified througha code analysis tool, a plurality of functional and sample tests for aplurality of new code are successful, and a plurality of recent windowfocus data, and wherein the plurality of concentration measures to whicha negative score is assigned are selected from a group consisting of alow quantity of lines of code produced, a slow consistency rate of workproduced, a plurality of historical productivity data, a plurality ofhistorical schedule data, a plurality of hormonal physiological effects,a plurality of brain physiological effects, a plurality of recent devicefocus data, a plurality of track micro/macro-expressions, a plurality ofgeneral health data, and a current noise level.
 13. The computer systemof claim 8, wherein the appropriate modification is based on acomparison of a usefulness of the identified potential distraction tothe user and an amount of distraction of the identified potentialdistraction to the user.
 14. The computer system of claim 13, whereinthe appropriate modification is selected from a group consisting ofreducing a volume level of a sound notification, shifting a notificationor window to a side of a graphical user interface, selecting anddisplaying only a plurality of useful information from a message,preventing a noise from playing until a later time, preventing anotification from executing until a later time, automatically setting auser status to away, and rescheduling a meeting to a later time.
 15. Acomputer program product for reducing one or more distractions during aperiod of high productivity, the computer program product comprising:one or more computer-readable tangible storage media and programinstructions stored on at least one of the one or more tangible storagemedia, the program instructions executable by a processor of a computerto perform a method, the method comprising: receiving a plurality ofuser metadata; in response to determining a user is in a highproductivity state, analyzing the plurality of received user metadatafor potential distractions to the high productivity state; in responseto identifying one or more potential distractions based on the pluralityof analyzed user metadata, determining an appropriate modification tothe one or more identified potential distractions; and performing thedetermined appropriate modification.
 16. The computer program product ofclaim 15, wherein the plurality of user metadata is selected from agroup consisting of a current open software application, a successfulcompletion of one or more scheduled tasks, a plurality of calendardetails, a chat history, a user typing speed, a user heart rate, a userperspiration level, a user blood pressure, one or more user facialexpressions, a user pupil dilation level, a user focus direction, andone or more user hand movements.
 17. The computer program product ofclaim 15, wherein determining the user is in a high productivity statefurther comprises: calculating a productivity score relative to a userbaseline using a weighted sum of a plurality of concentration measures,wherein the plurality of concentration measures assign a positive scoreto items that indicate a higher than average productivity and assign anegative score to items that indicate a lower than average productivity.18. The computer program product of claim 17, wherein the user baselineproductivity state is calculated based on an initial batch of usermetadata.
 19. The computer program product of claim 17, wherein theplurality of concentration measures to which a positive score isassigned are selected from a group consisting of a plurality of wellwritten source code identified through a code analysis tool, a pluralityof functional and sample tests for a plurality of new code aresuccessful, and a plurality of recent window focus data, and wherein theplurality of concentration measures to which a negative score isassigned are selected from a group consisting of a low quantity of linesof code produced, a slow consistency rate of work produced, a pluralityof historical productivity data, a plurality of historical scheduledata, a plurality of hormonal physiological effects, a plurality ofbrain physiological effects, a plurality of recent device focus data, aplurality of track micro/macro-expressions, a plurality of generalhealth data, and a current noise level.
 20. The computer program productof claim 15, wherein the appropriate modification is based on acomparison of a usefulness of the identified potential distraction tothe user and an amount of distraction of the identified potentialdistraction to the user.